Airport gate assignment performance indicator selection is a complicated decision‐making problem with strong subjectivity and difficulty in measuring the importance of each indicator. A better selection of performance indicators (PIs) can greatly increase the airport overall benefit. We adopt a multicriteria decision‐making approach to quantify qualitative PIs and conduct subsequent selection using the fuzzy clustering method. First, we identify 21 commonly used PIs through literature review and survey. Subsequently, the fuzzy analytic hierarchy process technique was employed to obtain the selection criteria weights based on the relative importance of significance, availability, and generalisability. Further, we aggregated the selection criteria weights and experts’ score to evaluate each PI for the clustering process. The fuzzy‐possibilistic product partition c‐means algorithm was applied to divide the PIs into different groups based on the three selection criteria as partitioning features. The cluster with highest weights of the centre was identified as the very high‐influence cluster, and 10 PIs were identified as a result. This study revealed that the passenger‐oriented objective is the most important performance criterion; however, the relevance of the airport/airline‐oriented and robustness‐oriented performance objectives was highlighted as well. It also offers a scientific approach to determine the objective functions for future gate assignment research. And, we believe, through slight modifications, this model can be used in other airports, other indicator selection problems, or other scenarios at the same airport to facilitate policy making and real situation practice, hence facilitate the management system for the airport.

Aircraft maintenance scheduling is a focus point for airlines. Maintenance is essential to ensure the airworthiness of aircraft, but it comes at the cost of rendering them unavailable for operations. In current operations, aircraft maintenance scheduling must often be updated to include time for non-routine (i.e., non-schedule) tasks. Non-routine maintenance tasks (NRTs) introduce significant uncertainty into aircraft maintenance scheduling, often leading to delays and increased operational costs. Despite their importance, limited research has been conducted on predicting NRT maintenance workload using data-driven methods. This study compares Random Forest and LGBM with a Weibull survival model for NRT workload prediction at the individual routine task (RT) level. Results indicate that model performance depends on data availability and the evaluation metric: using data from all tasks can help in sparse settings, while using an independent occurrence model to correct labor predictions may improve aggregate error at the cost of other outcomes. The historical-average baseline performed comparably to Independent LGBM on MAE, ROC-AUC, and average precision. Finally, results show that summing task-level predictions does not result into a good forecast for the work packages that contains all the tasks. Further data and prospective validation are needed before these estimates can guide work-package planning.